Review Current Forecasting Strategy And Propose A New One

Review current forecasting strategy and propose a new one

As the company prepares to meet demand and capacity requirements for its planned future growth, you have been asked to review the current forecasting strategy and help implement a new strategic plan for forecasting demand. The new forecasting plan ties directly to the overall strategic planning methodology established by the company. The company historically has used a time series method. The forecasting methods under consideration are the following: Qualitative, Simulation, Causal, and Time series. Using course materials and other research, identify which forecasting technique or multiple techniques should be used in the future for the company's strategy. Include consideration of other available techniques not listed. Explain the identified technique and provide examples of its usage in manufacturing, retail, and healthcare industries. Additionally, determine if any of the four listed techniques should NOT be used and justify why. Evaluate the significance of forecasting errors for the selected techniques and discuss the impact of such errors on forecasting accuracy and decision-making.

Paper For Above instruction

In strategic planning for demand forecasting, selecting the appropriate techniques is crucial for aligning operational objectives with market realities. Historically, the company relied on a time series approach, which utilizes historical data to predict future demand. While effective in certain contexts, expanding the forecasting arsenal to include other techniques can improve accuracy, especially in dynamic markets. Based on extensive research and industry applications, causal modeling and simulation methods are recommended as primary strategies, due to their ability to incorporate environmental variables and simulate complex customer behaviors.

Causal forecasting, which models relationships between demand and external factors, offers significant advantages in scenarios where such relationships are evident. For example, in manufacturing industries, causal models are used to predict product demand based on economic indicators or raw material prices (Makridakis, Wheelwright, & Hyndman, 1998). In the retail sector, causality between weather patterns and sales of seasonal products such as apparel or holiday items is exploited to optimize inventory management (Fildes et al., 2008). In healthcare, disease outbreak modeling is based on environmental factors and population behaviors, exemplifying causal linkages that enable proactive resource planning (Chowell et al., 2016).

Simulation techniques, which use computer models to imitate customer behavior, are increasingly vital in today's complex supply chains. Retailers, for instance, utilize discrete-event simulation to anticipate the effects of promotional campaigns on sales and inventory levels (Banks et al., 2010). Healthcare providers employ simulation to optimize patient flow and hospital resource allocation during fluctuating demand, enhancing service delivery (Saghafian, 2014). Manufacturing entities apply simulation to evaluate production line adjustments under different demand scenarios, reducing lead times and minimizing bottlenecks.

While these methods promise improved forecasting, it is essential to exclude certain techniques that may not align well with the company's strategic needs. Human judgment-based qualitative methods, although valuable when data is scarce, are prone to biases and inconsistency (Makridakis et al., 2018). As such, reliance solely on subjective judgment without quantitative backing can lead to inaccurate forecasts. Additionally, the company should avoid over-reliance on simple causal models if the demand-environment relationships are weak or poorly understood, as this can lead to misguided decisions.

Forecasting errors pose a significant challenge, impacting decision-making accuracy. Errors in causal models, if unrecognized, can result in overstocking or stockouts, affecting profitability and customer satisfaction. Quantifying forecast error through measures such as Mean Absolute Percentage Error (MAPE) helps evaluate model performance (Makridakis et al., 2018). The sensitivity of causal and simulation models to input data quality underscores the need for accurate, up-to-date environmental and customer data. An underestimation of demand variability can lead to insufficient capacity planning, while overestimation might result in excessive inventory costs.

In conclusion, integrating causal and simulation forecasting methods into the company's strategic plan will enhance demand prediction accuracy, facilitating better resource allocation and customer service. Caution should be exercised with qualitative judgment-based techniques to prevent biases, and the impact of forecast errors must be continually monitored to mitigate risks. These approaches aligned with rigorous data analysis and ongoing validation will position the company for sustainable growth in competitive markets.

References

• Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-event system simulation. Pearson Education.
• Chowell, G., Sattenspiel, L., Bansal, S., & Viboud, C. (2016). Mathematical models to characterize early epidemic growth: A review. Physics of life reviews, 18, 66-97.
• Fildes, R., R “” & et al. (2008). The impact of information quality on supply chain performance. European Journal of Operational Research, 184(3), 899-912.
• Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: methods and applications. John Wiley & Sons.
• Saghafian, S. (2014). Healthcare operations management: A systematic review. Manufacturing & Service Operations Management, 16(4), 478-491.